Cancer surgeries, in general, aim for complete tumor resection while minimizing patient morbidity. This balance is particularly challenging in brain tumor resections, where taking wide margins around a primary tumor can lead to significant damage to vital, cognitive, motor, or sensory functions. Therefore, margins tend to be kept to a minimum, increasing risk of leaving cancer behind. In these cases, it is particularly critical to be able to evaluate the completeness of surgery, yet existing methods—including pathology of excised tissue, pre- or intra-operative anatomical imaging guidance, and 5-ALA enhanced fluorescence guided surgery—have limited sensitivity and specificity for detecting residual cancer. In this article published by the group of Francisco E. Robles at Georgia Institute of Technology and Emory University, a quantitative oblique back illumination microscopy (qOBM) imaging system is demonstrated to be able to mimic the type of morphological specificity seen in hematoxylin and eosin (H&E) staining, but without requiring any extrinsic contrast enhancement and within 100-micron, 3D margins of an in vivo tissue surface. Since H&E is a gold standard for discriminating cancerous from healthy tissue at the cellular level in nearly all cancers, this technology has the potential to revolutionize margin assessment in surgical oncology as a whole, not only for brain cancer. Moreover, the wide-field imaging protocols and machine learning algorithms for automated cancer detection that this group is demonstrating in conjunction with qOBM further enhance the feasibility of transitioning this exciting technology to the clinic.

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